class_docstr: Our own implementation of stacked LSTM.
  Needed for the decoder, because we do input feeding.
code: def __init__(self, num_layers, input_size, hidden_size, dropout):
      super(StackedLSTM, self).__init__()
      self.dropout = nn.Dropout(dropout)
      self.num_layers = num_layers
      self.layers = nn.ModuleList()
  
      for _ in range(num_layers):
          self.layers.append(nn.LSTMCell(input_size, hidden_size))
          input_size = hidden_size
  
file_path: OpenNMT-py/onmt/modules/stacked_rnn.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new StackedLSTM object from inherited base classes: nn with example: obj = StackedLSTM(num_layers, input_size, hidden_size, dropout)